Overview

Dataset statistics

Number of variables17
Number of observations1235
Missing cells84
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory206.0 KiB
Average record size in memory170.8 B

Variable types

Categorical7
Numeric10

Alerts

AcPf_6M_APR has constant value "0" Constant
ID has a high cardinality: 447 distinct values High cardinality
Age is highly correlated with AcPf_6M_APRHigh correlation
ratio is highly correlated with TESTHigh correlation
G6PD is highly correlated with AcPf_6M_APRHigh correlation
EP_6M_AVT is highly correlated with AcPf_6M_AVT and 5 other fieldsHigh correlation
AcPf_6M_AVT is highly correlated with EP_6M_AVT and 5 other fieldsHigh correlation
EP_1AN_AVT is highly correlated with EP_6M_AVT and 5 other fieldsHigh correlation
AcPf_1AN_AVT is highly correlated with EP_6M_AVT and 5 other fieldsHigh correlation
EP_6M_APR is highly correlated with EP_6M_AVT and 5 other fieldsHigh correlation
EP_1AN_APR is highly correlated with EP_6M_AVT and 5 other fieldsHigh correlation
AcPf_1AN_APR is highly correlated with EP_6M_AVT and 5 other fieldsHigh correlation
Sexe is highly correlated with AcPf_6M_APRHigh correlation
TEST is highly correlated with ratioHigh correlation
Type_Hb is highly correlated with AcPf_6M_APRHigh correlation
Gpe_ABO is highly correlated with AcPf_6M_APRHigh correlation
Rhesus is highly correlated with AcPf_6M_APRHigh correlation
AcPf_6M_APR is highly correlated with TEST and 4 other fieldsHigh correlation
Type_Hb has 28 (2.3%) missing values Missing
Gpe_ABO has 28 (2.3%) missing values Missing
Rhesus has 28 (2.3%) missing values Missing
ID is uniformly distributed Uniform
G6PD has 34 (2.8%) zeros Zeros
EP_6M_AVT has 537 (43.5%) zeros Zeros
AcPf_6M_AVT has 1038 (84.0%) zeros Zeros
EP_1AN_AVT has 319 (25.8%) zeros Zeros
AcPf_1AN_AVT has 948 (76.8%) zeros Zeros
EP_6M_APR has 629 (50.9%) zeros Zeros
EP_1AN_APR has 499 (40.4%) zeros Zeros
AcPf_1AN_APR has 1044 (84.5%) zeros Zeros

Reproduction

Analysis started2023-01-13 09:43:50.944225
Analysis finished2023-01-13 09:44:14.025256
Duration23.08 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

Sexe
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size19.3 KiB
Fem
639 
Mas
596 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3705
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFem
2nd rowMas
3rd rowFem
4th rowMas
5th rowMas

Common Values

ValueCountFrequency (%)
Fem639
51.7%
Mas596
48.3%

Length

2023-01-13T09:44:14.121258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-13T09:44:14.245469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
fem639
51.7%
mas596
48.3%

Most occurring characters

ValueCountFrequency (%)
F639
17.2%
e639
17.2%
m639
17.2%
M596
16.1%
a596
16.1%
s596
16.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2470
66.7%
Uppercase Letter1235
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e639
25.9%
m639
25.9%
a596
24.1%
s596
24.1%
Uppercase Letter
ValueCountFrequency (%)
F639
51.7%
M596
48.3%

Most occurring scripts

ValueCountFrequency (%)
Latin3705
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F639
17.2%
e639
17.2%
m639
17.2%
M596
16.1%
a596
16.1%
s596
16.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3705
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F639
17.2%
e639
17.2%
m639
17.2%
M596
16.1%
a596
16.1%
s596
16.1%

Age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct450
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.95700405
Minimum3.4
Maximum90.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2023-01-13T09:44:14.382986image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3.4
5-th percentile6.1
Q111.7
median20.6
Q340
95-th percentile62.34
Maximum90.9
Range87.5
Interquartile range (IQR)28.3

Descriptive statistics

Standard deviation18.54322007
Coefficient of variation (CV)0.68788134
Kurtosis-0.3112305082
Mean26.95700405
Median Absolute Deviation (MAD)11.5
Skewness0.8201377808
Sum33291.9
Variance343.8510105
MonotonicityNot monotonic
2023-01-13T09:44:14.530315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2813
 
1.1%
3011
 
0.9%
2211
 
0.9%
1710
 
0.8%
1310
 
0.8%
459
 
0.7%
209
 
0.7%
7.89
 
0.7%
329
 
0.7%
108
 
0.6%
Other values (440)1136
92.0%
ValueCountFrequency (%)
3.42
0.2%
3.72
0.2%
3.81
0.1%
3.91
0.1%
41
0.1%
4.11
0.1%
4.21
0.1%
4.31
0.1%
4.41
0.1%
4.51
0.1%
ValueCountFrequency (%)
90.91
 
0.1%
891
 
0.1%
802
 
0.2%
783
0.2%
761
 
0.1%
75.11
 
0.1%
743
0.2%
72.91
 
0.1%
72.41
 
0.1%
726
0.5%

TEST
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size19.3 KiB
positif
971 
negatif
264 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters8645
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownegatif
2nd rowpositif
3rd rowpositif
4th rowpositif
5th rowpositif

Common Values

ValueCountFrequency (%)
positif971
78.6%
negatif264
 
21.4%

Length

2023-01-13T09:44:14.667810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-13T09:44:14.798932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
positif971
78.6%
negatif264
 
21.4%

Most occurring characters

ValueCountFrequency (%)
i2206
25.5%
t1235
14.3%
f1235
14.3%
p971
11.2%
o971
11.2%
s971
11.2%
n264
 
3.1%
e264
 
3.1%
g264
 
3.1%
a264
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8645
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i2206
25.5%
t1235
14.3%
f1235
14.3%
p971
11.2%
o971
11.2%
s971
11.2%
n264
 
3.1%
e264
 
3.1%
g264
 
3.1%
a264
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Latin8645
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i2206
25.5%
t1235
14.3%
f1235
14.3%
p971
11.2%
o971
11.2%
s971
11.2%
n264
 
3.1%
e264
 
3.1%
g264
 
3.1%
a264
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8645
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i2206
25.5%
t1235
14.3%
f1235
14.3%
p971
11.2%
o971
11.2%
s971
11.2%
n264
 
3.1%
e264
 
3.1%
g264
 
3.1%
a264
 
3.1%

ratio
Real number (ℝ≥0)

HIGH CORRELATION

Distinct637
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.071368421
Minimum0.32
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2023-01-13T09:44:14.961676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.32
5-th percentile1.09
Q12.175
median3.51
Q35.335
95-th percentile8.907
Maximum14
Range13.68
Interquartile range (IQR)3.16

Descriptive statistics

Standard deviation2.469950292
Coefficient of variation (CV)0.6066634205
Kurtosis1.032515639
Mean4.071368421
Median Absolute Deviation (MAD)1.52
Skewness1.087737084
Sum5028.14
Variance6.100654447
MonotonicityNot monotonic
2023-01-13T09:44:15.130111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.36
 
0.5%
3.276
 
0.5%
3.56
 
0.5%
3.046
 
0.5%
3.556
 
0.5%
2.586
 
0.5%
2.226
 
0.5%
3.976
 
0.5%
1.815
 
0.4%
3.395
 
0.4%
Other values (627)1177
95.3%
ValueCountFrequency (%)
0.322
0.2%
0.491
 
0.1%
0.531
 
0.1%
0.62
0.2%
0.621
 
0.1%
0.631
 
0.1%
0.641
 
0.1%
0.651
 
0.1%
0.71
 
0.1%
0.713
0.2%
ValueCountFrequency (%)
141
0.1%
13.541
0.1%
13.151
0.1%
13.021
0.1%
12.791
0.1%
12.631
0.1%
12.441
0.1%
12.381
0.1%
12.051
0.1%
11.991
0.1%

ID
Categorical

HIGH CARDINALITY
UNIFORM

Distinct447
Distinct (%)36.2%
Missing0
Missing (%)0.0%
Memory size19.3 KiB
18/04
 
6
20/06
 
5
03/02
 
5
07/12
 
5
17/10
 
5
Other values (442)
1209 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters6175
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87 ?
Unique (%)7.0%

Sample

1st row07/05
2nd row22/16
3rd row27/08
4th row06/25
5th row04/01

Common Values

ValueCountFrequency (%)
18/046
 
0.5%
20/065
 
0.4%
03/025
 
0.4%
07/125
 
0.4%
17/105
 
0.4%
19/075
 
0.4%
13/035
 
0.4%
18/135
 
0.4%
19/185
 
0.4%
03/225
 
0.4%
Other values (437)1184
95.9%

Length

2023-01-13T09:44:15.346290image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
18/046
 
0.5%
04/065
 
0.4%
29/045
 
0.4%
16/145
 
0.4%
07/025
 
0.4%
04/015
 
0.4%
15/045
 
0.4%
15/065
 
0.4%
09/095
 
0.4%
16/045
 
0.4%
Other values (437)1184
95.9%

Most occurring characters

ValueCountFrequency (%)
11240
20.1%
/1235
20.0%
01056
17.1%
2749
12.1%
3414
 
6.7%
4373
 
6.0%
6266
 
4.3%
8221
 
3.6%
5220
 
3.6%
7201
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4940
80.0%
Other Punctuation1235
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11240
25.1%
01056
21.4%
2749
15.2%
3414
 
8.4%
4373
 
7.6%
6266
 
5.4%
8221
 
4.5%
5220
 
4.5%
7201
 
4.1%
9200
 
4.0%
Other Punctuation
ValueCountFrequency (%)
/1235
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common6175
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11240
20.1%
/1235
20.0%
01056
17.1%
2749
12.1%
3414
 
6.7%
4373
 
6.0%
6266
 
4.3%
8221
 
3.6%
5220
 
3.6%
7201
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII6175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11240
20.1%
/1235
20.0%
01056
17.1%
2749
12.1%
3414
 
6.7%
4373
 
6.0%
6266
 
4.3%
8221
 
3.6%
5220
 
3.6%
7201
 
3.3%

Type_Hb
Categorical

HIGH CORRELATION
MISSING

Distinct6
Distinct (%)0.5%
Missing28
Missing (%)2.3%
Memory size19.3 KiB
AA
1082 
AS
 
99
AA+
 
10
AC
 
10
AS+
 
5

Length

Max length3
Median length2
Mean length2.012427506
Min length2

Characters and Unicode

Total characters2429
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowAS
2nd rowAA
3rd rowAA
4th rowAA
5th rowAA

Common Values

ValueCountFrequency (%)
AA1082
87.6%
AS99
 
8.0%
AA+10
 
0.8%
AC10
 
0.8%
AS+5
 
0.4%
AF1
 
0.1%
(Missing)28
 
2.3%

Length

2023-01-13T09:44:15.531247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-13T09:44:15.790415image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
aa1092
90.5%
as104
 
8.6%
ac10
 
0.8%
af1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A2299
94.6%
S104
 
4.3%
+15
 
0.6%
C10
 
0.4%
F1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2414
99.4%
Math Symbol15
 
0.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A2299
95.2%
S104
 
4.3%
C10
 
0.4%
F1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
+15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2414
99.4%
Common15
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A2299
95.2%
S104
 
4.3%
C10
 
0.4%
F1
 
< 0.1%
Common
ValueCountFrequency (%)
+15
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2429
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A2299
94.6%
S104
 
4.3%
+15
 
0.6%
C10
 
0.4%
F1
 
< 0.1%

G6PD
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct194
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.841206478
Minimum0
Maximum170.8
Zeros34
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2023-01-13T09:44:15.963958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q16.5
median8.6
Q311
95-th percentile18.8
Maximum170.8
Range170.8
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation11.19468474
Coefficient of variation (CV)1.137531741
Kurtosis108.9393718
Mean9.841206478
Median Absolute Deviation (MAD)2.2
Skewness9.188825874
Sum12153.89
Variance125.3209664
MonotonicityNot monotonic
2023-01-13T09:44:16.140137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1041
 
3.3%
034
 
2.8%
7.624
 
1.9%
8.522
 
1.8%
722
 
1.8%
9.521
 
1.7%
8.620
 
1.6%
11.820
 
1.6%
8.720
 
1.6%
10.518
 
1.5%
Other values (184)993
80.4%
ValueCountFrequency (%)
034
2.8%
0.124
 
0.3%
0.156
 
0.5%
0.165
 
0.4%
0.23
 
0.2%
0.232
 
0.2%
0.39
 
0.7%
0.352
 
0.2%
0.410
 
0.8%
0.465
 
0.4%
ValueCountFrequency (%)
170.81
 
0.1%
1434
0.3%
1291
 
0.1%
594
0.3%
352
0.2%
34.43
0.2%
323
0.2%
28.73
0.2%
26.72
0.2%
25.61
 
0.1%

Gpe_ABO
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.3%
Missing28
Missing (%)2.3%
Memory size19.3 KiB
O
533 
A
353 
B
268 
AB
 
53

Length

Max length2
Median length1
Mean length1.043910522
Min length1

Characters and Unicode

Total characters1260
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowA
3rd rowO
4th rowO
5th rowO

Common Values

ValueCountFrequency (%)
O533
43.2%
A353
28.6%
B268
21.7%
AB53
 
4.3%
(Missing)28
 
2.3%

Length

2023-01-13T09:44:16.303792image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-13T09:44:16.444360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
o533
44.2%
a353
29.2%
b268
22.2%
ab53
 
4.4%

Most occurring characters

ValueCountFrequency (%)
O533
42.3%
A406
32.2%
B321
25.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1260
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O533
42.3%
A406
32.2%
B321
25.5%

Most occurring scripts

ValueCountFrequency (%)
Latin1260
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O533
42.3%
A406
32.2%
B321
25.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1260
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O533
42.3%
A406
32.2%
B321
25.5%

Rhesus
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing28
Missing (%)2.3%
Memory size19.3 KiB
+
1129 
-
 
78

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1207
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row+
3rd row+
4th row+
5th row+

Common Values

ValueCountFrequency (%)
+1129
91.4%
-78
 
6.3%
(Missing)28
 
2.3%

Length

2023-01-13T09:44:16.604472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-13T09:44:16.720019image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1207
100.0%

Most occurring characters

ValueCountFrequency (%)
+1129
93.5%
-78
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Math Symbol1129
93.5%
Dash Punctuation78
 
6.5%

Most frequent character per category

Math Symbol
ValueCountFrequency (%)
+1129
100.0%
Dash Punctuation
ValueCountFrequency (%)
-78
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
+1129
93.5%
-78
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+1129
93.5%
-78
 
6.5%

EP_6M_AVT
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct13
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.28340081
Minimum0
Maximum13
Zeros537
Zeros (%)43.5%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2023-01-13T09:44:16.827898image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum13
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.795187542
Coefficient of variation (CV)1.398773889
Kurtosis6.717589107
Mean1.28340081
Median Absolute Deviation (MAD)1
Skewness2.298234018
Sum1585
Variance3.222698312
MonotonicityNot monotonic
2023-01-13T09:44:16.939141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0537
43.5%
1326
26.4%
2176
 
14.3%
381
 
6.6%
436
 
2.9%
530
 
2.4%
618
 
1.5%
810
 
0.8%
79
 
0.7%
96
 
0.5%
Other values (3)6
 
0.5%
ValueCountFrequency (%)
0537
43.5%
1326
26.4%
2176
 
14.3%
381
 
6.6%
436
 
2.9%
530
 
2.4%
618
 
1.5%
79
 
0.7%
810
 
0.8%
96
 
0.5%
ValueCountFrequency (%)
131
 
0.1%
112
 
0.2%
103
 
0.2%
96
 
0.5%
810
 
0.8%
79
 
0.7%
618
 
1.5%
530
 
2.4%
436
2.9%
381
6.6%

AcPf_6M_AVT
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2866396761
Minimum0
Maximum8
Zeros1038
Zeros (%)84.0%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2023-01-13T09:44:17.071199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8904907114
Coefficient of variation (CV)3.106655448
Kurtosis29.42301467
Mean0.2866396761
Median Absolute Deviation (MAD)0
Skewness4.875461685
Sum354
Variance0.7929737072
MonotonicityNot monotonic
2023-01-13T09:44:17.181194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
01038
84.0%
1130
 
10.5%
230
 
2.4%
317
 
1.4%
56
 
0.5%
45
 
0.4%
83
 
0.2%
73
 
0.2%
63
 
0.2%
ValueCountFrequency (%)
01038
84.0%
1130
 
10.5%
230
 
2.4%
317
 
1.4%
45
 
0.4%
56
 
0.5%
63
 
0.2%
73
 
0.2%
83
 
0.2%
ValueCountFrequency (%)
83
 
0.2%
73
 
0.2%
63
 
0.2%
56
 
0.5%
45
 
0.4%
317
 
1.4%
230
 
2.4%
1130
 
10.5%
01038
84.0%

EP_1AN_AVT
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.655870445
Minimum0
Maximum25
Zeros319
Zeros (%)25.8%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2023-01-13T09:44:17.321810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile9
Maximum25
Range25
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.379644585
Coefficient of variation (CV)1.272518617
Kurtosis7.625820316
Mean2.655870445
Median Absolute Deviation (MAD)2
Skewness2.4358967
Sum3280
Variance11.42199752
MonotonicityNot monotonic
2023-01-13T09:44:17.467654image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0319
25.8%
1277
22.4%
2205
16.6%
3123
 
10.0%
493
 
7.5%
651
 
4.1%
546
 
3.7%
724
 
1.9%
820
 
1.6%
917
 
1.4%
Other values (14)60
 
4.9%
ValueCountFrequency (%)
0319
25.8%
1277
22.4%
2205
16.6%
3123
 
10.0%
493
 
7.5%
546
 
3.7%
651
 
4.1%
724
 
1.9%
820
 
1.6%
917
 
1.4%
ValueCountFrequency (%)
251
 
0.1%
231
 
0.1%
211
 
0.1%
201
 
0.1%
191
 
0.1%
186
0.5%
171
 
0.1%
164
0.3%
154
0.3%
145
0.4%

AcPf_1AN_AVT
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct17
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6129554656
Minimum0
Maximum17
Zeros948
Zeros (%)76.8%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2023-01-13T09:44:17.605793image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum17
Range17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.732283022
Coefficient of variation (CV)2.82611563
Kurtosis26.91139657
Mean0.6129554656
Median Absolute Deviation (MAD)0
Skewness4.657111505
Sum757
Variance3.000804467
MonotonicityNot monotonic
2023-01-13T09:44:17.726493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0948
76.8%
1150
 
12.1%
248
 
3.9%
326
 
2.1%
415
 
1.2%
514
 
1.1%
69
 
0.7%
77
 
0.6%
106
 
0.5%
83
 
0.2%
Other values (7)9
 
0.7%
ValueCountFrequency (%)
0948
76.8%
1150
 
12.1%
248
 
3.9%
326
 
2.1%
415
 
1.2%
514
 
1.1%
69
 
0.7%
77
 
0.6%
83
 
0.2%
91
 
0.1%
ValueCountFrequency (%)
171
 
0.1%
151
 
0.1%
142
 
0.2%
131
 
0.1%
121
 
0.1%
112
 
0.2%
106
0.5%
91
 
0.1%
83
0.2%
77
0.6%

EP_6M_APR
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct14
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.089878543
Minimum0
Maximum13
Zeros629
Zeros (%)50.9%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2023-01-13T09:44:17.870736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum13
Range13
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.70541693
Coefficient of variation (CV)1.56477705
Kurtosis8.479968912
Mean1.089878543
Median Absolute Deviation (MAD)0
Skewness2.542089216
Sum1346
Variance2.908446906
MonotonicityNot monotonic
2023-01-13T09:44:17.989248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0629
50.9%
1305
24.7%
2134
 
10.9%
361
 
4.9%
440
 
3.2%
525
 
2.0%
617
 
1.4%
710
 
0.8%
86
 
0.5%
104
 
0.3%
Other values (4)4
 
0.3%
ValueCountFrequency (%)
0629
50.9%
1305
24.7%
2134
 
10.9%
361
 
4.9%
440
 
3.2%
525
 
2.0%
617
 
1.4%
710
 
0.8%
86
 
0.5%
91
 
0.1%
ValueCountFrequency (%)
131
 
0.1%
121
 
0.1%
111
 
0.1%
104
 
0.3%
91
 
0.1%
86
 
0.5%
710
 
0.8%
617
1.4%
525
2.0%
440
3.2%

AcPf_6M_APR
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.3 KiB
0
1235 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1235
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01235
100.0%

Length

2023-01-13T09:44:18.121561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-13T09:44:18.468794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
01235
100.0%

Most occurring characters

ValueCountFrequency (%)
01235
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1235
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01235
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1235
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01235
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1235
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01235
100.0%

EP_1AN_APR
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct21
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.896356275
Minimum0
Maximum21
Zeros499
Zeros (%)40.4%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2023-01-13T09:44:18.587331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile8
Maximum21
Range21
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.900949043
Coefficient of variation (CV)1.529748962
Kurtosis10.0479828
Mean1.896356275
Median Absolute Deviation (MAD)1
Skewness2.783485003
Sum2342
Variance8.415505351
MonotonicityNot monotonic
2023-01-13T09:44:18.722156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0499
40.4%
1285
23.1%
2145
 
11.7%
386
 
7.0%
468
 
5.5%
538
 
3.1%
631
 
2.5%
720
 
1.6%
817
 
1.4%
910
 
0.8%
Other values (11)36
 
2.9%
ValueCountFrequency (%)
0499
40.4%
1285
23.1%
2145
 
11.7%
386
 
7.0%
468
 
5.5%
538
 
3.1%
631
 
2.5%
720
 
1.6%
817
 
1.4%
910
 
0.8%
ValueCountFrequency (%)
211
 
0.1%
201
 
0.1%
192
 
0.2%
183
0.2%
172
 
0.2%
151
 
0.1%
145
0.4%
135
0.4%
127
0.6%
114
0.3%

AcPf_1AN_APR
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct14
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3781376518
Minimum0
Maximum14
Zeros1044
Zeros (%)84.5%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2023-01-13T09:44:18.838084image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.331092372
Coefficient of variation (CV)3.520126507
Kurtosis40.64210422
Mean0.3781376518
Median Absolute Deviation (MAD)0
Skewness5.757823407
Sum467
Variance1.771806902
MonotonicityNot monotonic
2023-01-13T09:44:18.953849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
01044
84.5%
1104
 
8.4%
237
 
3.0%
313
 
1.1%
410
 
0.8%
57
 
0.6%
76
 
0.5%
84
 
0.3%
63
 
0.2%
112
 
0.2%
Other values (4)5
 
0.4%
ValueCountFrequency (%)
01044
84.5%
1104
 
8.4%
237
 
3.0%
313
 
1.1%
410
 
0.8%
57
 
0.6%
63
 
0.2%
76
 
0.5%
84
 
0.3%
91
 
0.1%
ValueCountFrequency (%)
141
 
0.1%
132
 
0.2%
121
 
0.1%
112
 
0.2%
91
 
0.1%
84
 
0.3%
76
0.5%
63
 
0.2%
57
0.6%
410
0.8%

Interactions

2023-01-13T09:44:11.124460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:43:56.888583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:43:58.323621image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:00.011659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:01.695171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:03.267036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:05.020822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:06.480133image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:08.056763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:09.608883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:11.265075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:43:57.011525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:43:58.456727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:00.202770image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:01.865170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:03.406667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:05.172076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:06.631826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:08.188234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:09.730054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:11.430047image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:43:57.150848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:43:58.618190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:00.365064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:02.010972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:03.546757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:05.298177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:06.796124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:08.357161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:09.892804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:11.608714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:43:57.304334image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:43:58.814656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:00.489661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:02.144895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:03.709257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:05.433389image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:06.945812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:08.544480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:10.055899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:11.794486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:43:57.454073image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:43:58.979814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:00.658798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:02.272334image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:03.879450image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:05.583361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:07.086714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:08.693439image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:10.203796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:11.948862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:43:57.617142image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:43:59.105117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:00.824747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:02.472848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:04.067546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:05.731798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:07.246163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:08.877563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:10.366405image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:12.101412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:43:57.765081image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:43:59.299227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:01.021691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:02.661626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:04.211266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:05.852229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:07.390672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:09.010778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:10.524737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:12.230062image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:43:57.908268image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:43:59.484204image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:01.166688image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:02.792497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:04.349413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:06.012197image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:07.566063image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:09.159169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:10.662124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:12.388994image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:43:58.079357image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:43:59.643391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:01.376024image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:02.947994image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:04.495197image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:06.157447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:07.729927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:09.323556image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:10.794243image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:12.540325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:43:58.195403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:43:59.798986image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:01.524920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:03.079075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:04.850671image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:06.306236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:07.871530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:09.460272image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-13T09:44:10.978995image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-13T09:44:19.086028image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2023-01-13T09:44:19.323244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-01-13T09:44:19.554855image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-01-13T09:44:19.787030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-01-13T09:44:20.024716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-01-13T09:44:20.209659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-01-13T09:44:13.062785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-13T09:44:13.522343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-13T09:44:13.756324image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2023-01-13T09:44:13.890863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

SexeAgeTESTratioIDType_HbG6PDGpe_ABORhesusEP_6M_AVTAcPf_6M_AVTEP_1AN_AVTAcPf_1AN_AVTEP_6M_APRAcPf_6M_APREP_1AN_APRAcPf_1AN_APR
0Fem23.0negatif1.007/05AS8.50B-00413040
1Mas8.8positif2.022/16AA25.00A+42753075
2Fem13.1positif2.027/08AA7.00O+10101021
3Mas5.0positif3.006/25AA5.10O+00111010
4Mas72.4positif3.004/01AA8.70O+00100000
5Fem10.4positif4.015/04AA0.15B+85181040128
6Fem4.5positif4.018/29AA15.30O+7313570133
7Mas7.1positif4.004/21AA10.20B+31311010
8Mas17.9positif5.016/41AA170.80O-00003030
9Mas22.0positif6.003/06AA0.35A+10201020

Last rows

SexeAgeTESTratioIDType_HbG6PDGpe_ABORhesusEP_6M_AVTAcPf_6M_AVTEP_1AN_AVTAcPf_1AN_AVTEP_6M_APRAcPf_6M_APREP_1AN_APRAcPf_1AN_APR
1225Mas78.0positif9.6216/01AA7.05O+00000010
1226Fem58.5positif9.7009/02AA6.80B+10100010
1227Fem47.0positif9.7615/06AA12.50O+31313050
1228Fem22.9positif9.7719/18AA13.50O+20306070
1229Fem7.8positif9.7936/04AA5.20A-00000000
1230Fem63.0positif9.8514/18AA9.10O+10100010
1231Mas45.7positif9.8514/23AS0.80O+00000000
1232Fem58.9positif9.8908/05AS10.80A+00202020
1233Fem54.0positif9.9108/05AS10.80A+00110000
1234Fem20.1positif9.9503/19AA11.00O+00001060